Since the advent of the first electronic computers in the 1940's, computers have continued to handle a greater variety of increasingly complex tasks. Advances in semiconductors and other hardware components have evolved to the point that current low-end desktop computers can now handle tasks that once required roomfuls of computers.
Computer programs, which are essentially the sets of instructions that control the operation of a computer to perform tasks, have also grown increasingly complex and powerful. While early computer programs were limited to performing only basic mathematical calculations, current computer programs handle complex tasks such as voice and image recognition, predictive analysis and forecasting, multimedia presentation, and other tasks that are too numerous to mention.
However, one common characteristic of many computer programs is that the programs are typically limited to performing tasks in response to specific commands issued by an operator or user. A user therefore must often know the specific controls, commands, etc. required to perform specific tasks. As computer programs become more complex and feature rich, users are called upon to learn and understand more and more about the programs to take advantage of the improved functionality.
In addition to being more powerful, computers have also become more interconnected through private networks such as local area networks and wide area networks, and through public networks such as the Internet. This enables computers and their users to interact and share information with one another on a global scale. However, the amount of information is increasing at an exponential rate, which makes it increasingly difficult for users to find specific information.
As a result of the dramatic increases in the both complexity of computer programs and the amount of information available to users, substantial interest has developed in the area of intelligent agent computer programs, also referred to as intelligent agents or simply agents, that operate much like software-implemented "assistants" to automate and simplify certain tasks in a way that hides their complexity from the user. With agents, a user may be able to perform tasks without having to know specific sequences of commands. Similarly, a user may be able to obtain information without having to know exactly how or where to search for the information.
Intelligent agents are characterized by the concept of delegation, where a user, or client, entrusts the agents to handle tasks with at least a certain degree of autonomy. Intelligent agents operate with varying degrees of constraints depending upon the amount of autonomy that is delegated to them by the user.
Intelligent agents may also have differing capabilities in terms of intelligence, mobility, agency, and user interface. Intelligence is generally the amount of reasoning and decision making that an agent possesses. This intelligence can be as simple as following a predefined set of rules, or as complex as learning and adapting based upon a user's objectives and the agent's available resources.
Mobility is the ability to be passed through a network and execute on different computer systems. That is, some agents may be designed to stay on one computer system and may never be passed to different machines, while other agents may be mobile in the sense that they are designed to be passed from computer to computer while performing tasks at different stops along the way. User interface defines how an agent interacts with a user, if at all.
Agents have a number of uses in a wide variety of applications, including systems and network management, mobile access and management, information access and management, collaboration, messaging, workflow and administrative management, and adaptive user interfaces. Another important use for agents is in electronic commerce, where an agent may be configured to seek out other parties such as other users, computer systems and agents, conduct negotiations on behalf of their client, and enter into commercial transactions.
Just as human agents have a certain amount of autonomy, intelligent agents similarly have a set of constraints on what they are authorized and not authorized to do. For example, a selling agent for electronic commerce applications may be constrained by a minimum acceptable price. However, a good selling agent, whether electronic or human, would never initially give its lowest acceptable price, as this would minimize profit margins. Furthermore, giving the lowest price may not even assure sales because a buyer may infer that the price is not competitive because the agent is unwilling to lower the price from the original offer. Therefore, an agent typically starts negotiations with some margin from its worst case acceptable price, then works toward a mutually acceptable price with the other party.
Any negotiation plans, techniques, strategies or other confidential information used by an intelligent agent to operate within its constraints, however, often should be hidden from other parties. Otherwise, the agent is placed at a competitive disadvantage. Given that many agents may be dispatched to unsecured environments, an assumption must be made that other parties may be able to scan or reverse engineer an agent to learn its negotiation strategy or other constraints. It must also be assumed that other parties may be able to decode messages sent between an agent and its client to obtain the greatest advantage in negotiation. The validity of such assumptions stems from the fact that these techniques are conceptually similar to many of the techniques used by some salespeople to obtain the best price possible.
For example, if a message to an agent from its client indicates that the agent should offer $100, but is authorized to go as low as $90, another party that intercepts this message immediately knows that a transaction may be completed at the lower price. Also, even if the message is not intercepted, if the agent has stored the $90 price as its lowest authorized offer, reverse compilation or scanning of the agent by another party may lead to similar results.
Efforts have been made to encrypt messages between an agent and its client. However, most conventional encryption methods rely on private "keys" for the agent and the client. Encryption presupposes that both the sender and receiver are in secured environments--only the transmission path between them is unsecured. However, as an agent may be resident on and executing in an unsecured environment, the agent may be reverse compiled or scanned to obtain its private key and thereby break the encryption. Consequently, conventional encryption techniques do not adequately protect the confidential communications of agents or other computer programs executing in unsecured environments.
Therefore, a significant need exists in the art for a manner of protecting the confidential information of an intelligent agent computer program.